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    Unmanned Aerial Vehicles (UAVs) are already broadly employed for 3D modeling of large objects such as trees and monuments via photogrammetry. The usual workflow includes two distinct steps: image acquisition with UAV and computationally demanding postflight image processing. Insufficient feature overlaps across images is a common shortcoming in post-flight image processing resulting in the failure of 3D reconstruction. Here we propose a real-time control system that overcomes this limitation by targeting specific spatial locations for image acquisition thereby providing sufficient feature overlap. We initially benchmark several implementations of the Scale-Invariant Feature Transform (SIFT) feature identification algorithm to determine whether they allow real-time execution on the low-cost processing hardware embedded on the UAV. We then experimentally test our UAV platform in virtual and real-life environments. The presented architecture consistently decreases failures and improves the overall quality of 3D reconstructions.

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Liénard, Jean; Vogs, Andre; Gatziolis, Demetrios; Strigul, Nikolay. 2016. Embedded, real-time UAV control for improved, image-based 3D scene reconstruction. Measurement, Vol. 81: 6 pages.: 264-269.


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    3D reconstruction, Aerial robotics, Computer vision, Robotics in agriculture and forestry, Real-time photogrammetry.

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